[Device Part4] AI Milestones: A Journey Through History

Welcome to "Artificial Intelligent (AI) Device," the definitive series at the cutting edge of semiconductor device fabrication. In our premiere episode, since we plan to explore AI devices like AI accelerators and high bandwidth memory (HBM), it’s crucial to understand the ongoing developments and specific needs regarding semiconductor devices, even though our channel primarily focuses on semiconductor device hardware. This episode traces AI's evolution from its conceptualization by Alan Turing to the current boom in technology. It's particularly suited for semiconductor professionals who are new to the AI field. Whether you're a novice or an expert, this series aims to expand your understanding of the crucial roles semiconductor devices play in modern electronics. Don't forget to like, subscribe, and click the bell icon to stay updated with our enlightening content. Below is a detailed outline of this episode; click any timestamp to jump directly to a specific section.

Episode Outline:

[0:00] Intro: Overview of what we'll cover in this episode.
[0:32] Alan Turing's Imitation Game: The inception of AI concepts.
[2:54] Biological vs. Artificial Neurons: Comparative analysis.
[3:50] Basics of Neural Networks: Understanding logical operations.
[4:35] The Rise of Perceptron: Early neural network models.
[5:08] Perceptron Training & Prediction: How perceptrons learn and make decisions.
[7:28] Rosenblatt's Demonstration (1957): Discriminating between images of men and women.
[8:42] XOR Problem: Highlighting the Perceptron's limitations with non-linear decision functions.
[9:25] Real-Life Implications of XOR: Exploring scenarios that challenge Perceptron’s capabilities.
[10:10] Limitations of Perceptron: Inability to solve non-linear problems like XOR.
[10:45] First AI Winter: Triggered by limitations in learning complex patterns.
[11:25] Multilayer Perceptron & Backpropagation: Solutions that enable learning XOR and other complex problems.
[12:55] CNN (Convolutional Neural Network): Advances in recognizing handwritten ZIP codes.
[14:25] Second AI Winter: Challenges in scaling neural networks due to computational limits.
[15:45] AI Renaissance: A convergence of advanced algorithms, increased computational power, internet proliferation, big data, and significant tech investments revive and accelerate AI evolution.
[16:30] IBM’s Deep Blue Triumph (1997): Victory over Chess Champion Kasparov.
[18:40] ImageNet (2012): CNN's triumph in an image recognition contest.
[21:10] Google’s AlphaGo Triumph (2016): AI victory over Go Champion Sedol Lee.
[22:00] Complexity of Chess vs. Go: Comparing the computational challenges.
[23:00] OpenAI's ChatGPT: Introduction to generative AI and large language models (LLM).
[25:45] Wrap-up: Discussing how AI may not always outperform experts but generally exceeds average human performance across many areas economically.